Biopython SeqIO to Pandas Dataframe

Question

I have a FASTA file that can easily be parsed by SeqIO.parse.

I am interested in extracting sequence ID's and sequence lengths. I used these lines to do it, but I feel it's waaaay too heavy (two iterations, conversions, etc.)

from Bio import SeqIO
import pandas as pd


# parse sequence fasta file
identifiers = [seq_record.id for seq_record in SeqIO.parse("sequence.fasta",
                                                           "fasta")]
lengths = [len(seq_record.seq) for seq_record in SeqIO.parse("sequence.fasta",
                                                             "fasta")]
#converting lists to pandas Series    
s1 = Series(identifiers, name='ID')
s2 = Series(lengths, name='length')
#Gathering Series into a pandas DataFrame and rename index as ID column
Qfasta = DataFrame(dict(ID=s1, length=s2)).set_index(['ID'])

I could do it with only one iteration, but I get a dict :

records = SeqIO.parse(fastaFile, 'fasta')

and I somehow can't get DataFrame.from_dict to work...

My goal is to iterate the FASTA file, and get ids and sequences lengths into a DataFrame through each iteration.

Here is a short FASTA file for those who want to help.

Answer 1

David has given you a nice answer on the pandas side, on the Biopython side you don't need to use SeqRecord objects via Bio.SeqIO if all you want is the record identifiers and their sequence length - this should be faster:

from Bio.SeqIO.FastaIO import SimpleFastaParser
with open('sequences.fasta') as fasta_file:  # Will close handle cleanly
    identifiers = []
    lengths = []
    for title, sequence in SimpleFastaParser(fasta_file):
        identifiers.append(title.split(None, 1)[0])  # First word is ID
        lengths.append(len(sequence))

Answer 2

You're spot on - you definitely shouldn't be parsing the file twice, and storing the data in a dictionary is a waste of computing resources when you'll just be converting it to numpy arrays later.

SeqIO.parse() returns a generator, so you can iterate record-by-record, building a list like so:

with open('sequences.fasta') as fasta_file:  # Will close handle cleanly
    identifiers = []
    lengths = []
    for seq_record in SeqIO.parse(fasta_file, 'fasta'):  # (generator)
        identifiers.append(seq_record.id)
        lengths.append(len(seq_record.seq))

See Peter Cock's answer for a more efficient way of parsing just ID's and sequences from a FASTA file.

The rest of your code looks pretty good to me. However, if you really want to optimize for use with pandas, you can read below:

---

On minimizing memory usage

Consulting the source of panda.Series, we can see that data is stored interally as a numpy ndarray:

class Series(np.ndarray, Picklable, Groupable):
    """Generic indexed series (time series or otherwise) object.

    Parameters
    ----------
    data:  array-like
        Underlying values of Series, preferably as numpy ndarray

If you make identifiers an ndarray, it can be used directly in Series without constructing a new array (the parameter copy, default False) will prevent a new ndarray being created if not needed. By storing your sequences in a list, you'll force Series to coerce said list to an ndarray.

Avoid initializing lists

If you know in advance exactly how many sequences you have (and how long the longest ID will be), you could initialize an empty ndarray to hold identifiers like so:

num_seqs = 50
max_id_len = 60
numpy.empty((num_seqs, 1), dtype='S{:d}'.format(max_id_len))

Of course, it's pretty hard to know exactly how many sequences you'll have, or what the largest ID is, so it's easiest to just let numpy convert from an existing list. However, this is technically the fastest way to store your data for use in pandas.